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Cloudera Iceberg MCP Server

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by cloudera
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Python
Remote

Cloudera Iceberg MCP Server (via Impala)

This is a A Model Context Protocol server that provides read-only access to Iceberg tables via Apache Impala. This server enables LLMs to inspect database schemas and execute read-only queries.

  • execute_query(query: str): Run any SQL query on Impala and return the results as JSON.

  • get_schema(): List all tables available in the current database.

Usage with Claude Desktop

To use this server with the Claude Desktop app, add the following configuration to the "mcpServers" section of your claude_desktop_config.json:

Option 1: Direct installation from GitHub (Recommended)

{
  "mcpServers": {
    "iceberg-mcp-server": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/cloudera/iceberg-mcp-server@main",
        "run-server"
      ],
      "env": {
        "IMPALA_HOST": "coordinator-default-impala.example.com",
        "IMPALA_PORT": "443",
        "IMPALA_USER": "username",
        "IMPALA_PASSWORD": "password",
        "IMPALA_DATABASE": "default"
      }
    }
  }
}

Option 2: Local installation (after cloning the repository)

{
  "mcpServers": {
    "iceberg-mcp-server": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/iceberg-mcp-server",
        "run",
        "src/iceberg_mcp_server/server.py"
      ],
      "env": {
        "IMPALA_HOST": "coordinator-default-impala.example.com",
        "IMPALA_PORT": "443",
        "IMPALA_USER": "username",
        "IMPALA_PASSWORD": "password",
        "IMPALA_DATABASE": "default"
      }
    }
  }
}

For Option 2, replace /path/to with your path to this repository. Set the environment variables according to your Impala configuration.

Related MCP server: IcebergMCP

Usage with AI frameworks

The ./examples folder contains several examples how to integrate this MCP Server with common AI Frameworks like LangChain/LangGraph, OpenAI SDK.

Transport

The MCP server's transport protocol is configurable via the MCP_TRANSPORT environment variable. Supported values:

  • stdio (default) — communicate over standard input/output. Useful for local tools, command-line scripts, and integrations with clients like Claude Desktop.

  • http - expose an HTTP server. Useful for web-based deployments, microservices, exposing MCP over a network.

  • sse — use Server-Sent Events (SSE) transport. Useful for existing web-based deployments that rely on SSE.

Copyright (c) 2025 - Cloudera, Inc. All rights reserved.

Available Tools

2 tools
execute_queryC

Execute a SQL query on the Impala database and return results as JSON.

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYes

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the return format ('as JSON') which is helpful, but doesn't address critical behavioral aspects like authentication requirements, query execution limits, error handling, whether this is read-only or can modify data, or performance characteristics. For a database query tool with zero annotation coverage, this is insufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise - a single sentence that efficiently communicates the core functionality. Every word earns its place: 'Execute' (action), 'SQL query' (what), 'on the Impala database' (where), 'and return results as JSON' (output format). No wasted words or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a database query execution tool with no annotations, no output schema, and minimal parameter documentation, the description is incomplete. It doesn't address safety considerations (read vs write operations), authentication needs, error scenarios, or result formatting details beyond 'JSON'. The agent would need to make assumptions about many critical aspects of tool behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the schema provides no parameter documentation. The description doesn't elaborate on the 'query' parameter beyond what's implied by the tool name. It doesn't specify SQL dialect, query limitations, or parameter formatting requirements. However, with only one parameter, the baseline expectation is lower, and the description at least confirms this is a SQL query parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Execute a SQL query') and target resource ('on the Impala database'), making the purpose immediately understandable. However, it doesn't distinguish this tool from its sibling 'get_schema' - both could involve database operations, so the distinction isn't explicit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance about when to use this tool versus alternatives. There's no mention of the sibling tool 'get_schema' or any context about when direct query execution is appropriate versus schema inspection. The description simply states what the tool does without usage context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_schemaA

Retrieve the list of table names in the current Impala database.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

A3.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It describes the basic operation but lacks behavioral details such as whether this requires specific permissions, how results are formatted (e.g., list, JSON), if there are rate limits, or if it's cached. For a tool with zero annotation coverage, this is a significant gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, clear sentence that efficiently conveys the tool's purpose without any wasted words. It is appropriately sized and front-loaded with the essential information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is adequate but incomplete. It lacks details on behavioral aspects like return format or error handling, which are important for a tool with no structured output schema to guide the agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description appropriately omits parameter details, earning a baseline score of 4 for not adding unnecessary information.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Retrieve') and resource ('list of table names in the current Impala database'), distinguishing it from the sibling tool 'execute_query' which presumably runs queries rather than listing metadata.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context ('current Impala database') but does not explicitly state when to use this tool versus alternatives like querying system tables directly or using other metadata tools. No exclusions or prerequisites are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 2 tool updatesv1.0.0
    • First observedexecute_query
    • First observedget_schema
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TDQS

B3.3/5.0
Disambiguation5/5

The two tools have completely distinct purposes: execute_query runs SQL queries and returns results, while get_schema retrieves metadata about table names. There is no overlap in functionality or ambiguity about when to use each tool.

Naming Consistency5/5

Both tools follow a consistent verb_noun naming pattern (execute_query, get_schema) with clear action-object relationships. The naming style is uniform throughout the set.

Tool Count2/5

With only 2 tools for a database/query server, the surface feels severely limited. While the tools cover basic query execution and schema inspection, typical database operations like table creation, data manipulation, or metadata exploration beyond table names are missing, making this feel under-scoped.

Completeness2/5

For an Impala/Cloudera Iceberg database server, the toolset is significantly incomplete. There are no tools for creating/dropping tables, inserting/updating data, managing partitions, or accessing detailed schema information beyond table names. This creates dead ends for agents trying to perform common database operations.

Maintenance

ActivityInactive
ResponsivenessSyncing

Resources

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Related MCP Connectors

Related MCP Servers

  • IcebergMCP

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    A
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    C
    quality
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    5
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  • horizon-mcp

    kshash919
    F
    license
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    Enables natural language querying of Snowflake-managed Iceberg tables via Horizon IRC and DuckDB, without using a Snowflake warehouse.
    -

  • iceberg-mcp-server-hive

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    Provides read-only SQL access to Apache Iceberg tables via HiveServer2, enabling querying, schema discovery, and database listing on Cloudera Data Platform.
    3
    Apache 2.0

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